"""simple docstring"""



import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def 	_lowerCamelCase(	a				):
       __a       =    [
           "decoder.version",
           "decoder.output_projection.weight",
           "_float_tensor",
           "decoder.embed_positions._float_tensor",
       ]
       for k in ignore_keys:
              state_dict.pop(a   ,    a				)



def 	_lowerCamelCase(	a				):
       __a ,   __a       =    emb.weight.shape
       __a       =    nn.Linear(a   ,    a   ,    bias=a				)
       __a       =    emb.weight.data
       return lin_layer



def 	_lowerCamelCase(	a				):
       __a       =    torch.load(a   ,    map_location="cpu"				)
       __a       =    Namespace(**checkpoint["cfg"]["model"]				)
       __a       =    checkpoint["model"]
       remove_ignore_keys_(a				)
       __a       =    state_dict["decoder.embed_tokens.weight"].shape[0]

       __a       =    {key.replace("decoder"   ,    "model"				): val for key, val in state_dict.items()}

       __a       =    XGLMConfig(
           vocab_size=a   ,    max_position_embeddings=args.max_target_positions   ,    num_layers=args.decoder_layers   ,    attention_heads=args.decoder_attention_heads   ,    ffn_dim=args.decoder_ffn_embed_dim   ,    d_model=args.decoder_embed_dim   ,    layerdrop=args.decoder_layerdrop   ,    dropout=args.dropout   ,    attention_dropout=args.attention_dropout   ,    activation_dropout=args.activation_dropout   ,    activation_function="gelu"   ,    scale_embedding=not args.no_scale_embedding   ,    tie_word_embeddings=args.share_decoder_input_output_embed   ,    )

       __a       =    XGLMForCausalLM(a				)
       __a       =    model.load_state_dict(a   ,    strict=a				)
       print(a				)
       __a       =    make_linear_from_emb(model.model.embed_tokens				)

       return model


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Optional[Any]									=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
   parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   SCREAMING_SNAKE_CASE__:str									=			parser.parse_args()
   SCREAMING_SNAKE_CASE__:Dict									=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
   model.save_pretrained(args.pytorch_dump_folder_path)

"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


SCREAMING_SNAKE_CASE__:Dict									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        warnings.warn(
            "The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
            " use DonutImageProcessor instead."    ,		lowerCamelCase    ,		)
        super().__init__(*lowerCamelCase    ,		**lowerCamelCase						)
